[Solved] NLTK ImportError: cannot import name ‘stopwords’

Last Updated on 2021-08-30 by Clay

Problem

Today, when I was processing the English corpus, I used NLTK, a classic natural language processing tool in Python, to segment the text again. But when I use the following program to use the following program to import stopwords:

from nltk.corpus import stopwords

Yes, I encountered a strange error message:

ImportError: cannot import name 'stopwords'

This is a strange problem. I also specially opened a new virtual environment to try to reinstall NLTK, and found that it should be no problem to directly use the above code to import, the only possibility is that someone accidentally stored it in the stopword in the nltk package was accidentally deleted…

the magic thing is that this has happened for the second time, so I have the experience of the last time and quickly found a solution. Rather, many people’s problems with stopwords are more bizarre and difficult to solve than mine… Hahaha.

Solution

At present, most of the problems I have encountered are that I cannot find the stopwords in the nltk package, so I just need to use the program to download it again.

import nltk
nltk.download('stopwords')

Output:

[nltk_data] Downloading package stopwords to
[nltk_data] /home/clay/nltk_data…
[nltk_data] Unzipping corpora/stopwords.zip.
True

Seeing the above message, it probably means that the download was successful. Next, let’s try it right away to see if it works normally.

from nltk.corpus import stopwords
print(stopwords.words('english'))

Output:

['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]

If a lot of words are printed, it is likely that the disabled words can be successfully used.

References

• https://stackoverflow.com/questions/26693736/nltk-and-stopwords-fail-lookuperror
• https://github.com/heroku/heroku-buildpack-python/issues/685

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